The role of wavelength, power density and melanin tPBM-induced BOLD fMRI response
Bibliographic record
Abstract
Objective: One major clinical challenge of repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses to rTMS exhibited high individual variations.Anatomical factors that may contribute to the heterogeneity in rTMS effects on depression and cognition, and rTMS-induced neuroplastic changes, are less investigated.Methods: Fifty-five older patients with co-occurring depression and cognitive impairments were randomly assigned to receive either active or sham rTMS on left dorsolateral prefrontal cortex (DLPFC).Individual's brain age was calculated with morphometric features using support vector machine (SVM).Brain-predicted age difference (brain-PAD) was computed as the difference between estimated brain age and chronological age.The changes of motor threshold (MT) were used to evaluate the neuroplasticity.Results: The rTMS responders and remitters had younger brain age.Every additional year of brain-PAD at baseline decreased the odds of the relief of depressive symptoms by ~25.7% in responders (Odd ratio [OR] 0.743, Nagelkerke R 2 0.392, p 0.045) and by ~39.5% in remitters (OR 0.605, Nagelkerke R 2 0.606, p 0.022) at 3 rd week in active rTMS group.Using brain-PAD as feature, responder-nonresponder classification accuracies of 85% (3 rd week) and 84% (12 th week), respectively were achieved.Conclusion: Pre-treatment brain age matrices by macro-level morphometric features in patients with neurocognitive disorders, may be relevant to inter-individual variability in treatment responses to rTMS treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".